Update README.md

README.md

@@ -1,3 +1,63 @@
 ---
+language: 
+  - en
+
+tags:
+- pytorch
+- ner
+- qa
+
+inference: false
+
 license: mit
+
+datasets:
+- conll2003
+
+metrics:
+- f1
 ---
+# t5-base-qa-ner-conll
+
+Unofficial implementation of [InstructionNER](https://arxiv.org/pdf/2203.03903v1.pdf).
+t5-base model tuned on conll2003 dataset.
+
+https://github.com/ovbystrova/InstructionNER 
+
+## Inference 
+```shell
+git clone https://github.com/ovbystrova/InstructionNER 
+cd InstructionNER
+```
+
+```python
+from instruction_ner.model import Model
+model = Model(
+    model_path_or_name="olgaduchovny/t5-base-ner-mit-movie",
+    tokenizer_path_or_name="olgaduchovny/t5-base-ner-mit-movie"
+)
+options = ["LOC", "PER", "ORG", "MISC"]
+instruction = "please extract entities and their types from the input sentence, " \
+              "all entity types are in options"
+text = "The protest , which attracted several thousand supporters , coincided with the 18th anniversary of Spain 's constitution ."
+generation_kwargs = {
+    "num_beams": 2,
+    "max_length": 128
+}
+pred_spans = model.predict(
+    text=text,
+    generation_kwargs=generation_kwargs,
+    instruction=instruction,
+    options=options
+)
+>>> [(99, 104, 'LOC')]
+```
+
+## Prediction Sample
+```
+Sentence: The protest , which attracted several thousand supporters , coincided with the 18th anniversary of Spain 's constitution .
+Instruction: please extract entities and their types from the input sentence, all entity types are in options
+Options: ORG, PER, LOC
+Prediction (raw text): Spain is a LOC.
+Prediction (span): [(99, 104, 'LOC')]
+```